ZnO–Carbon Photocatalysis Dataset Generated via Langmuir–Hinshelwood Model

This dataset contains synthetic data generated through numerical solutions of the Langmuir–Hinshelwood (L–H) kinetic model to simulate photocatalytic degradation processes using ZnO–carbon nanocomposites.

The dataset covers a broad range of physicochemical parameters, including initial pollutant concentration, pH, temperature, irradiation time, catalyst particle size, bandgap energy, absorbance, reaction rate constant, catalyst loading, and light intensity.

The Langmuir–Hinshelwood model was selected because it accounts for both adsorption equilibrium and surface reaction kinetics, making it well-suited for modeling heterogeneous photocatalytic systems.

This dataset is intended to support data-driven modeling, optimization studies, and machine learning applications in photocatalysis, particularly for predicting degradation efficiency under varying operational conditions.

The dataset is associated with a study on a hybrid BBDML framework developed for photocatalytic performance prediction and optimization.

All data points are synthetically generated but constrained within experimentally reported ranges to ensure realistic representation of photocatalytic behavior.